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Data Science and Machine Learning Basic to Advance

SAP Course Overview
Data Science and Machine Learning

Introduction to the Course

This course introduces the fundamentals of data science and Machine learning, providing a solid foundation for aspiring data scientists. It covers essential topics like data handling, analysis, and visualization.

Who is this course for?

  • Freshers with any Graduation degree
  • Professionals who want to switch from NON-IT to IT
  • Professionals who want to boost there career

Requirements

  • Basic understanding of Python Programming Language
  • We'll use Anaconda & Jupyter Notebooks (a free, user-friendly coding environment)
  • Familiarity with basic Python knowledge is strongly recommended
  • Basic Programming concepts like Variables, Data Types and Basic Arithmetic
  • Basic Control Flows like Conditional Statements, Loops, Break and Continue

What You'll Learn

Python▶
  • Introduction to Python.
  • User Interface or IDE (Anaconda and collab).
  • Python architecture.
  • Data types, Tokens.
  • Keywords, Variables.
  • Operators (assignment, comparison, comparative, arithmetic, Logical, Conditional, Bitwise).
  • Type casting.
  • Input and output functions.
  • String manipulation (Indexing, slicing, functions, Access elements, Reversing).
  • Data structures and its manipulation (List, tuple, dictionary, set, Frozen set).
  • Control flow statements (if, else, elif, Nested if).
  • Loops (for, while, Nested loop, loop else).
  • Break, continue, pass, range statements and usage.
  • List comprehension.
  • Functions (Inbuilt and user defined functions).
  • Invoking function, passing arguments into function, return keyword.
  • Local and global variables.
  • Lambda function (map, filter, reduce, zip).
  • Introduction to Libraries (OS, datetime, calendar, sys, math).
  • Recursive function and usage.
  • File Operations (read, write, open, save).
  • Serialization and deserialization.
  • Exception handling (default exception and errors, catch exceptions, Try except-else-finally block, user defined exceptions).
  • Regular expressions (Match, search, grouping, Flag).
  • Iterators, Generators, Decorators.
Numpy▶
  • Order to Cash Cycle. Creating NumPy arrays.
  • Downloading and parsing data.
  • Indexing and slicing.
  • Multidimensional array.
  • Array views and copies.
Pandas▶
  • Series and DataFrame.
  • Multilevel series.
  • Grouping data in DataFrame.
  • Aggregate functions, Merge, concatenate and joining DataFrames.
  • Manipulating data, Data wrangling, understanding data using panda.
Plots▶
  • Different types of plots using matplotlib and seaborn library.
  • Subplots, labelling and arranging plots, save plots.
  • Regression plots.
  • Heat map, distribution plot, correlation plot, style functions.
EDA▶
  • Univariate, Bivariate, and multivariate analysis.
  • Multicollinearity, encoding techniques.
  • Standardization and Normalization.
  • Interpretation data using plots.
  • Data cleaning and Handling null values.
SQL▶
  • Data, Information, Database, Table, Types of databases, Schema.
  • Install of MySQL workbench and data import options.
  • Data types, comments.
  • Date Definition Language (DDL), Data Manipulation Language (DML), Data Query Language (DQL), Transaction Control Language (TCL), Data control Language (DCL) statements.
  • Operators (Arithmetic, Comparison, Logical, Bitwise).
  • Boolean expression, date expression.
  • SQL clauses.
  • SQL constraints.
  • Aggregate and Non-aggregate functions.
  • Unions, Types of keys, Joins, Views.
  • Subqueries.
  • Window functions.
Stats for Data Science▶
  • Mean, Median, Mode, Variance, Standard deviation.
  • Z score, t-distribution, Z vs T stats.
  • Probability distribution function, Binomial distribution, Normal distribution.
  • Bias variance dichotomy.
  • Sample vs population stats.
  • Sampling and t-test, Random variables.
  • Central Limit theorem.
  • Covariance, Correlation.
  • Hypothesis testing, Type 1, Type 2 error.
  • One sample test, two sample test (independent test, dependent test).
  • Chi-square test, ANOVA test.
  • Parametric and Non-Parametric testing.
  • Stats using SciPy library.
  • Feature selection.
Machine Learning▶
  • Intro to machine learning.
  • Mathematical background of machine learning.
  • Data split, Test train validate datasets
  • Supervised learning: Linear regression, multilinear regression, polynomial regression, Ridge regression, Lasso regression, Elastic net regression, Logistic regression, R2, Adjusted R2.
  • Unsupervised learning: Decision tree, Random Forest, Naïve Bayes, KNN classification, K-Means, DBscan clustering, PCA, Support Vector Machine, Out of Bag evaluation, XGBoost, AdaBoost, Metrics for classification (confusion matrix, ROC_AUC, F1 score, Accuracy, Precision, Recall), overfitting and underfitting.
  • Model building, Hyper parameter tuning.

Trainer Expertise

This program is monitored by a team of professionals. We have crafted this program using the learnings of 23+ years of experience handling corporate training and job oriented training. Our students are working in almost all top MNCs across India.

Job Opportunities

100% placement record — each student successfully transitioned into a desired Data Science and Machine Learning career role.

Course Duration

16 Weeks

Fees

Training + Job Assistance: ₹35,000

  • Admission: ₹10,000
  • After 1 month: ₹25,000
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